def stariy_hm_loss(pred, target, coef=1.0):
pred_mes = UniformMeasure2DFactory.from_heatmap(pred)
target_mes = UniformMeasure2DFactory.from_heatmap(target)
return Loss(nn.BCELoss()(pred, target) * coef +
nn.MSELoss()(pred_mes.coord, target_mes.coord) *
(0.001 * coef) +
nn.L1Loss()(pred_mes.coord, target_mes.coord) * (0.001 * coef))
def verka(encoder: nn.Module):
res = []
for i, (image, lm) in enumerate(LazyLoader.celeba_test(64)):
content = encoder(image.cuda())
mes = UniformMeasure2D01(lm.cuda())
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content)
res.append(Samples_Loss(p=1)(mes, pred_measures).item() * image.shape[0])
return np.mean(res)/len(LazyLoader.celeba_test(1).dataset)
def forward(self, image: Tensor):
img_content = self.gh(image)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(
img_content)
sparce_hm = self.heatmaper.forward(pred_measures.coord * 63)
return pred_measures, sparce_hm
def hm_svoego_roda_loss(pred, target, coef=1.0, l1_coef=0.0):
pred_mes = UniformMeasure2DFactory.from_heatmap(pred)
target_mes = UniformMeasure2DFactory.from_heatmap(target)
# pred = pred.relu() + 1e-15
# target[target < 1e-7] = 0
# target[target > 1 - 1e-7] = 1
if torch.isnan(pred).any() or torch.isnan(target).any():
print("nan in hm")
return Loss.ZERO()
bce = nn.BCELoss()(pred, target)
if torch.isnan(bce).any():
print("nan in bce")
return Loss.ZERO()
return Loss(bce * coef + nn.MSELoss()(pred_mes.coord, target_mes.coord) *
(0.0005 * coef) +
nn.L1Loss()(pred_mes.coord, target_mes.coord) * l1_coef)
def liuboff(encoder: nn.Module):
sum_loss = 0
for i, batch in enumerate(LazyLoader.w300().test_loader):
data = batch['data'].to(device)
landmarks = batch["meta"]["keypts_normalized"].cuda()
landmarks[landmarks > 1] = 0.99999
# content = heatmap_to_measure(encoder(data))[0]
pred_measure = UniformMeasure2DFactory.from_heatmap(encoder(data))
target = UniformMeasure2D01(torch.clamp(landmarks, max=1))
eye_dist = landmarks[:, 45] - landmarks[:, 36]
eye_dist = eye_dist.pow(2).sum(dim=1).sqrt()
sum_loss += (handmadew1(pred_measure, target) / eye_dist).sum().item()
return sum_loss / len(LazyLoader.w300().test_dataset)
def train_content(cont_opt, R_b, R_t, real_img, heatmaper, g_transforms):
requires_grad(encoder_HG, True)
coefs = json.load(open("../parameters/content_loss_sup.json"))
# pred_measures, sparse_hm = encoder_HG(real_img)
content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content)
sparse_hm = heatmaper.forward(pred_measures.coord * 63)
ll = (
writable("R_b", R_b.__call__)(real_img, pred_measures) * coefs["R_b"] +
writable("Sparse", hm_loss_bes_xy)(content, sparse_hm.detach()) * coefs["Sparse"] +
writable("R_t", R_t.__call__)(real_img, sparse_hm) * coefs["R_t"]
)
ll.minimize_step(cont_opt)
def liuboff(encoder: nn.Module):
sum_loss = 0
for i, batch in enumerate(LazyLoader.mafl().test_loader):
data = batch['data'].to(device)
landmarks = batch["meta"]["keypts_normalized"].cuda().type(dtype=torch.float32)
landmarks[landmarks > 1] = 0.99999
# content = heatmap_to_measure(encoder(data))[0]
pred_measure = UniformMeasure2DFactory.from_heatmap(encoder(data))
target = UniformMeasure2D01(torch.clamp(landmarks, max=1))
eye_dist = landmarks[:, 1] - landmarks[:, 0]
eye_dist = eye_dist.pow(2).sum(dim=1).sqrt()
# w1_loss = (handmadew1(pred_measure, target) / eye_dist).sum().item()
# l1_loss = ((pred_measure.coord - target.coord).pow(2).sum(dim=2).sqrt().mean(dim=1) / eye_dist).sum().item()
# print(w1_loss, l1_loss)
sum_loss += ((pred_measure.coord - target.coord).pow(2).sum(dim=2).sqrt().mean(dim=1) / eye_dist).sum().item()
return sum_loss / len(LazyLoader.mafl().test_dataset)
def train_content(cont_opt, tuner_verka, heatmaper, encoder_HG, R_b, R_t,
real_img):
requires_grad(encoder_HG, True)
img_content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(
img_content)
# sparce_hm = blur(
# heatmaper.forward(pred_measures.probability, pred_measures.coord * 63).detach()
# )
sparce_hm = heatmaper.forward(pred_measures.coord * 63).detach()
# sparce_hm = sparse_heatmap(img_content).detach()
ll = tuner_verka.sum_losses([
writable("R_b", R_b.__call__)(real_img, pred_measures) * 0.5,
writable("Sparse", hm_svoego_roda_loss)(img_content, sparce_hm, 500) *
0.5,
writable("R_t", R_t.__call__)(real_img, sparce_hm) * 2.5
])
# print(i, ll.item())
ll.minimize_step(cont_opt)
def coord_hm_loss(pred_coord: Tensor, target_hm: Tensor, coef=1.0):
target_hm = target_hm / target_hm.sum(dim=[2, 3], keepdim=True)
target_hm = target_hm.detach()
heatmapper = ToGaussHeatMap(256, 4)
target_coord = UniformMeasure2DFactory.from_heatmap(
target_hm).coord.detach()
# sk = CoordToGaussSkeleton(target_hm.shape[-1], 1)
# pred_sk = sk.forward(pred_coord).sum(dim=1, keepdim=True)
# target_sk = sk.forward(target_coord).sum(dim=1, keepdim=True).detach()
pred_hm = heatmapper.forward(pred_coord).sum(dim=1, keepdim=True)
pred_hm = pred_hm / pred_hm.sum(dim=[2, 3], keepdim=True).detach()
target_hm = heatmapper.forward(target_coord).sum(dim=1,
keepdim=True).detach()
target_hm = target_hm / target_hm.sum(dim=[2, 3], keepdim=True).detach()
return Loss(nn.BCELoss()(pred_hm, target_hm) * coef * 1.5 +
# noviy_hm_loss(pred_sk, target_sk, coef).to_tensor() * 0.5 +
nn.MSELoss()(pred_coord, target_coord) * (0.001 * coef) +
nn.L1Loss()(pred_coord, target_coord) * (0.001 * coef))
def do_train(real_img):
batch_size = real_img.shape[0]
requires_grad(encoder_HG, True)
requires_grad(generator, False)
requires_grad(model.loss.discriminator, False)
# pred_measures, sparse_hm = encoder_HG(real_img)
img_content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(img_content)
sparse_hm = heatmaper.forward(pred_measures.coord * 63).detach()
# restored = decoder(sparse_hm, style_encoder(real_img))
trans = g_transforms(image=real_img, mask=img_content)
trans_content, trans_image = trans["mask"], trans["image"]
restored = decoder(img_content, style_encoder(trans_image))
noise1 = mixing_noise(batch_size, latent_size, 0.9, device)
fake1, _ = generator(trans_content, noise1)
trans_fake_content = encoder_HG(fake1.detach())
coefs = json.load(open("../parameters/content_loss.json"))
tuner.sum_losses([
writable("Fake-content D", model.loss.generator_loss)(
real=None,
fake=[fake1, img_content.detach()]) * coefs["Fake-content D"], # 50 000
writable("Real-content D", model.loss.discriminator_loss_as_is)(
[real_img, img_content],
[fake1.detach(), img_content]) * coefs["Real-content D"], # 3000
writable("R_b", R_b.__call__)(real_img, pred_measures) * coefs["R_b"], # 3000
writable("Sparse", hm_loss_bes_xy)(img_content, sparse_hm) * coefs["Sparse"], # 1.5
writable("R_t", R_t.__call__)(real_img, sparse_hm) * coefs["R_t"], # 3
L1("L1 image")(restored, real_img) * coefs["L1 image"], # 10
writable("fake_content loss", stariy_hm_loss)(
trans_fake_content, trans_content
) * coefs["fake_content loss"] # 6477
]).minimize_step(
cont_opt
)
def do_train(real_img):
batch_size = real_img.shape[0]
requires_grad(encoder_HG, True)
img_content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(
img_content)
sparce_hm = heatmaper.forward(pred_measures.coord * 63)
restored = decoder(sparce_hm, style_encoder(real_img))
noise1 = mixing_noise(batch_size, latent_size, 0.9, device)
fake1, _ = generator(img_content, noise1)
cont_fake1 = encoder_HG(fake1.detach())
coefs = json.load(open("../parameters/content_loss_sup.json"))
tuner.sum_losses([
writable("Fake-content D", model.loss.generator_loss)(
real=None, fake=[fake1, img_content.detach()]) *
coefs["Fake-content D"], # 800
writable("Real-content D", model.loss.generator_loss)(
real=None, fake=[real_img, img_content]) *
coefs["Real-content D"], # 5
writable("R_b", R_b.__call__)(real_img, pred_measures) *
coefs["R_b"], # 3000
writable("Sparse", hm_svoego_roda_loss)(
img_content, sparce_hm.detach(), 500) * coefs["Sparse"], # 500
writable("R_t", R_t.__call__)(real_img, img_content) *
coefs["R_t"], # 16155
L1("L1 image")(restored, real_img) * coefs["L1 image"], # 10
writable("fake_content loss", hm_svoego_roda_loss)(
cont_fake1, img_content.detach(), 500, 0.1) *
coefs["fake_content loss"] # 6477
]).minimize_step(cont_opt)
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device, starting_model_number):
latent_size = 512
batch_size = 12
sample_z = torch.randn(8, latent_size, device=device)
MAFL.batch_size = batch_size
MAFL.test_batch_size = 64
Celeba.batch_size = batch_size
test_img = next(LazyLoader.mafl().loader_train_inf)["data"][:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
style_opt = optim.Adam(style_encoder.parameters(), lr=5e-4, betas=(0.9, 0.99))
cont_opt = optim.Adam(encoder_HG.parameters(), lr=2e-5, betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(albumentations.Compose([
ResizeMask(h=256, w=256),
albumentations.ElasticTransform(p=0.7, alpha=150, alpha_affine=1, sigma=10),
albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
ResizeMask(h=64, w=64),
NormalizeMask(dim=(0, 1, 2))
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img:
# rt_loss(encoder_HG(trans_dict['image']), trans_dict['mask'])
stariy_hm_loss(encoder_HG(trans_dict['image']), trans_dict['mask'])
)
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt:
L1("R_s")(ltnt, style_encoder(trans_dict['image']))
)
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_5").cuda().batch_repeat(batch_size)
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 4.0)
tuner = GoldTuner([0.37, 1.55, 0.9393, 0.1264, 1.7687, 0.8648, 1.8609], device=device, rule_eps=0.01 / 2,
radius=0.1, active=True)
heatmaper = ToGaussHeatMap(64, 1.0)
sparse_bc = heatmaper.forward(barycenter.coord * 63)
sparse_bc = nn.Upsample(scale_factor=4)(sparse_bc).sum(dim=1, keepdim=True).repeat(1, 3, 1, 1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
sparse_bc = (sparse_bc - sparse_bc.min()) / sparse_bc.max()
send_images_to_tensorboard(writer, sparse_bc, "BC", 0, normalize=False, range=(0, 1))
trainer_gan = gan_trainer(model, generator, decoder, encoder_HG, style_encoder, R_s, style_opt, heatmaper,
g_transforms)
content_trainer = content_trainer_with_gan(cont_opt, tuner, heatmaper, encoder_HG, R_b, R_t, model, generator,
g_transforms)
supervise_trainer = content_trainer_supervised(cont_opt, encoder_HG, LazyLoader.mafl().loader_train_inf)
for i in range(100000):
counter.update(i)
requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.mafl().loader_train_inf)["data"].to(device) \
if i % 5 == 0 else next(LazyLoader.celeba().loader).to(device)
img_content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(img_content)
sparse_hm = heatmaper.forward(pred_measures.coord * 63).detach()
trainer_gan(i, real_img, pred_measures.detach(), sparse_hm.detach(), apply_g=False)
supervise_trainer()
if i % 4 == 0:
# real_img = next(LazyLoader.mafl().loader_train_inf)["data"].to(device)
trainer_gan(i, real_img, pred_measures.detach(), sparse_hm.detach(), apply_g=True)
content_trainer(real_img)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss.json"))
print(i, coefs)
with torch.no_grad():
# pred_measures_test, sparse_hm_test = encoder_HG(test_img)
content_test = encoder_HG(test_img)
pred_measures_test: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content_test)
heatmaper_256 = ToGaussHeatMap(256, 2.0)
sparse_hm_test = heatmaper.forward(pred_measures_test.coord * 63)
sparse_hm_test_1 = heatmaper_256.forward(pred_measures_test.coord * 255)
latent_test = style_encoder(test_img)
sparce_mask = sparse_hm_test_1.sum(dim=1, keepdim=True)
sparce_mask[sparce_mask < 0.0003] = 0
iwm = imgs_with_mask(test_img, sparce_mask)
send_images_to_tensorboard(writer, iwm, "REAL", i)
fake_img, _ = generator(sparse_hm_test, [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
restored = decoder(sparse_hm_test, latent_test)
iwm = imgs_with_mask(restored, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "RESTORED", i)
content_test_256 = nn.Upsample(scale_factor=4)(sparse_hm_test).sum(dim=1, keepdim=True).repeat(1, 3, 1,
1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
content_test_256 = (content_test_256 - content_test_256.min()) / content_test_256.max()
send_images_to_tensorboard(writer, content_test_256, "HM", i, normalize=False, range=(0, 1))
if i % 50 == 0 and i >= 0:
test_loss = liuboff(encoder_HG)
# test_loss = nadbka(encoder_HG)
tuner.update(test_loss)
writer.add_scalar("liuboff", test_loss, i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.module.state_dict(),
'd': discriminator.module.state_dict(),
'c': encoder_HG.module.state_dict(),
"s": style_encoder.state_dict()
},
f'{Paths.default.models()}/stylegan2_MAFL_{str(i + starting_model_number).zfill(6)}.pt',
)
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device,
starting_model_number):
latent_size = 512
batch_size = 8
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
W300DatasetLoader.batch_size = batch_size
W300DatasetLoader.test_batch_size = 16
test_img = next(LazyLoader.w300().loader_train_inf)["data"][:8].cuda()
model = CondStyleGanModel(generator, StyleGANLoss(discriminator),
(0.001 / 4, 0.0015 / 4))
style_opt = optim.Adam(style_encoder.parameters(),
lr=5e-4,
betas=(0.9, 0.99))
cont_opt = optim.Adam(encoder_HG.parameters(), lr=3e-5, betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(
albumentations.Compose([
albumentations.ElasticTransform(p=0.7,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.9, rotate_limit=15),
])),
ToTensor(device),
])
g_transforms_without_norm: albumentations.DualTransform = albumentations.Compose(
[
ToNumpy(),
NumpyBatch(
albumentations.Compose([
albumentations.ElasticTransform(p=0.3,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img: coord_hm_loss(
encoder_HG(trans_dict['image'])["coords"], trans_dict['mask']))
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt: WR.L1("R_s")
(ltnt, style_encoder(trans_dict['image'])))
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_68").cuda().batch_repeat(
batch_size)
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 4.0)
tuner = GoldTuner([0.37, 2.78, 0.58, 1.43, 3.23],
device=device,
rule_eps=0.001,
radius=0.3,
active=False)
trainer_gan = gan_trainer(model, generator, decoder, encoder_HG,
style_encoder, R_s, style_opt, g_transforms)
content_trainer = content_trainer_with_gan(cont_opt, tuner, encoder_HG,
R_b, R_t, model, generator,
g_transforms, decoder,
style_encoder)
supervise_trainer = content_trainer_supervised(
cont_opt, encoder_HG,
LazyLoader.w300().loader_train_inf)
for i in range(11000):
WR.counter.update(i)
requires_grad(encoder_HG, False)
real_img = next(LazyLoader.celeba().loader).to(device)
encoded = encoder_HG(real_img)
internal_content = encoded["skeleton"].detach()
trainer_gan(i, real_img, internal_content)
# content_trainer(real_img)
train_content(cont_opt, R_b, R_t, real_img, model, encoder_HG, decoder,
generator, style_encoder)
supervise_trainer()
encoder_ema.accumulate(encoder_HG.module, i, 0.98)
if i % 50 == 0 and i > 0:
encoder_ema.write_to(encoder_HG.module)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss.json"))
print(i, coefs)
with torch.no_grad():
# pred_measures_test, sparse_hm_test = encoder_HG(test_img)
encoded_test = encoder_HG(test_img)
pred_measures_test: UniformMeasure2D01 = UniformMeasure2D01(
encoded_test["coords"])
heatmaper_256 = ToGaussHeatMap(256, 1.0)
sparse_hm_test_1 = heatmaper_256.forward(
pred_measures_test.coord)
latent_test = style_encoder(test_img)
sparce_mask = sparse_hm_test_1.sum(dim=1, keepdim=True)
sparce_mask[sparce_mask < 0.0003] = 0
iwm = imgs_with_mask(test_img, sparce_mask)
send_images_to_tensorboard(WR.writer, iwm, "REAL", i)
fake_img, _ = generator(encoded_test["skeleton"], [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures_test.toImage(256))
send_images_to_tensorboard(WR.writer, iwm, "FAKE", i)
restored = decoder(encoded_test["skeleton"], latent_test)
iwm = imgs_with_mask(restored, pred_measures_test.toImage(256))
send_images_to_tensorboard(WR.writer, iwm, "RESTORED", i)
content_test_256 = (encoded_test["skeleton"]).repeat(1, 3, 1, 1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
content_test_256 = (content_test_256 - content_test_256.min()
) / content_test_256.max()
send_images_to_tensorboard(WR.writer,
content_test_256,
"HM",
i,
normalize=False,
range=(0, 1))
if i % 50 == 0 and i >= 0:
test_loss = liuboff(encoder_HG)
print("liuboff", test_loss)
# test_loss = nadbka(encoder_HG)
tuner.update(test_loss)
WR.writer.add_scalar("liuboff", test_loss, i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.module.state_dict(),
'd': discriminator.module.state_dict(),
'c': encoder_HG.module.state_dict(),
"s": style_encoder.state_dict(),
"e": encoder_ema.storage_model.state_dict()
},
f'{Paths.default.models()}/stylegan2_new_{str(i + starting_model_number).zfill(6)}.pt',
)
NumpyBatch(
albumentations.Compose([
albumentations.ElasticTransform(p=0.7,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.9, rotate_limit=15),
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms_2, lambda trans_dict, img: coord_hm_loss(
hg(trans_dict['image'])["mes"].coord, trans_dict['mask']))
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/cardio_200").cuda().batch_repeat(batch_size)
R_b = BarycenterRegularizer.__call__(barycenter, 0.5, 1.0, 5.0)
def train_content(cont_opt, R_b, R_t, real_img, encoder_HG):
# heatmapper = ToGaussHeatMap(256, 4)
requires_grad(encoder_HG, True)
coefs = json.load(
open(os.path.join(sys.path[0], "../parameters/content_loss.json")))
encoded = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = encoded["mes"]
heatmap_content = encoded["hm"]
def train(generator, decoder, discriminator, discriminatorHG, encoder_HG, style_encoder, device, starting_model_number):
latent_size = 512
batch_size = 12
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
W300DatasetLoader.batch_size = batch_size
W300DatasetLoader.test_batch_size = 64
encoder_HG_supervised = HG_softmax2020(num_classes=68, heatmap_size=64)
encoder_HG_supervised.load_state_dict(torch.load(f'{Paths.default.models()}/hg2_e29.pt', map_location="cpu"))
encoder_HG_supervised = encoder_HG_supervised.cuda()
requires_grad(encoder_HG_supervised, False)
style_opt = optim.Adam(style_encoder.parameters(), lr=5e-4, betas=(0.9, 0.99))
test_img = next(LazyLoader.celeba().loader)[:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
loss_st2: StyleGANLoss = StyleGANLoss(discriminatorHG)
model2 = CondStyleGanModel(encoder_HG, loss_st2, (2e-5, 0.0005))
heatmaper = ToGaussHeatMap(64, 1.0)
# wide_heatmaper = HeatMapToGaussHeatMap(64, 5.0)
tuda_trainer = gan_tuda_trainer(model, generator)
obratno_trainer = gan_obratno_trainer(model2, encoder_HG)
tuda_obratno_trainer = gan_tuda_obratno_trainer(generator, encoder_HG, decoder, style_encoder, style_opt)
for i in range(100000):
counter.update(i)
# requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.celeba().loader).to(device)
heatmap = encoder_HG_supervised(real_img).detach()
# heatmap = wide_heatmaper.forward(encoder_HG_supervised(real_img)).detach()
# heatmap = encoder_HG_supervised(next(LazyLoader.celeba().loader).to(device)).detach()
# tuda_trainer(real_img, heatmap)
obratno_trainer(real_img, heatmap)
# tuda_obratno_trainer(real_img, heatmap)
# pred_hm = encoder_HG(real_img)
# stariy_hm_loss(pred_hm, heatmap).minimize_step(model2.optimizer.opt_min)
if i % 100 == 0:
with torch.no_grad():
content_test = encoder_HG(test_img)
pred_measures_test: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content_test)
heatmaper_256 = ToGaussHeatMap(256, 2.0)
sparse_hm_test = heatmaper.forward(pred_measures_test.coord * 63)
sparse_hm_test_1 = heatmaper_256.forward(pred_measures_test.coord * 255)
latent_test = style_encoder(test_img)
sparce_mask = sparse_hm_test_1.sum(dim=1, keepdim=True)
sparce_mask[sparce_mask < 0.0003] = 0
iwm = imgs_with_mask(test_img, sparce_mask)
send_images_to_tensorboard(writer, iwm, "REAL", i)
fake_img, _ = generator(sparse_hm_test, [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
restored = decoder(sparse_hm_test, latent_test)
iwm = imgs_with_mask(restored, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "RESTORED", i)
content_test_256 = nn.Upsample(scale_factor=4)(sparse_hm_test).sum(dim=1, keepdim=True).repeat(1, 3, 1, 1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
content_test_256 = (content_test_256 - content_test_256.min()) / content_test_256.max()
send_images_to_tensorboard(writer, content_test_256, "HM", i, normalize=False, range=(0, 1))
if i % 50 == 0 and i >= 0:
test_loss = liuboff(encoder_HG)
print("liuboff", test_loss)
# test_loss = nadbka(encoder_HG)
# tuner.update(test_loss)
writer.add_scalar("liuboff", test_loss, i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.module.state_dict(),
'd': discriminator.module.state_dict(),
'c': encoder_HG.module.state_dict(),
"s": style_encoder.state_dict(),
"d2": discriminatorHG.state_dict()
},
f'{Paths.default.models()}/cyclegan_{str(i + starting_model_number).zfill(6)}.pt',
)
ResizeMask(h=256, w=256),
albumentations.ElasticTransform(p=1,
alpha=150,
alpha_affine=1,
sigma=10),
# albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
ResizeMask(h=64, w=64),
NormalizeMask(dim=(0, 1, 2))
])),
ToTensor(device),
])
with torch.no_grad():
from matplotlib import pyplot as plt
content_test = encoder_HG(test_img)
pred_measures = UniformMeasure2DFactory.from_heatmap(content_test)
sparce_hm = heatmaper.forward(pred_measures.coord * 63).detach()
fake_img, _ = generator(sparce_hm, noise)
iwm = imgs_with_mask(fake_img, pred_measures.toImage(256))
plt.imshow(((iwm[0].permute(1, 2, 0) + 1) / 2).cpu().numpy())
plt.show()
trans_dict = g_transforms(image=test_img, mask=sparce_hm)
sparce_hm = trans_dict["mask"]
pred_measures = UniformMeasure2DFactory.from_heatmap(sparce_hm)
#iwm = imgs_with_mask(test_img, pred_measures.toImage(256))
fake_img, _ = generator(sparce_hm, noise)
iwm = imgs_with_mask(fake_img, pred_measures.toImage(256))
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device,
starting_model_number):
latent_size = 512
batch_size = 24
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
test_img = next(LazyLoader.celeba().loader)[:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
style_opt = optim.Adam(style_encoder.parameters(),
lr=5e-4,
betas=(0.9, 0.99))
cont_opt = optim.Adam(encoder_HG.parameters(), lr=4e-5, betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(
albumentations.Compose([
ResizeMask(h=256, w=256),
albumentations.ElasticTransform(p=0.7,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
ResizeMask(h=64, w=64),
NormalizeMask(dim=(0, 1, 2))
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img: hm_svoego_roda_loss(
encoder_HG(trans_dict['image']), trans_dict['mask'], 1000, 0.3))
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt: L1("R_s")
(ltnt, style_encoder(trans_dict['image'])))
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_68").cuda().batch_repeat(
batch_size)
# plt.imshow(barycenter.toImage(256)[0][0].detach().cpu().numpy())
# plt.show()
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 3.0)
# 4.5, 1.2, 1.12, 1.4, 0.07, 2.2
# 1.27, 3.55, 5.88, 3.83, 2.17, 0.22, 1.72
tuner = GoldTuner([2.2112, 2.3467, 3.8438, 3.2202, 2.0494, 0.0260, 5.8378],
device=device,
rule_eps=0.03,
radius=1,
active=True)
# tuner_verka = GoldTuner([3.0, 1.2, 2.0], device=device, rule_eps=0.05, radius=1, active=True)
best_igor = 100
heatmaper = ToGaussHeatMap(64, 1.5)
trainer_gan = gan_trainer(model, generator, decoder, encoder_HG,
style_encoder, R_s, style_opt, heatmaper,
g_transforms)
content_trainer = content_trainer_with_gan(cont_opt, tuner, heatmaper,
encoder_HG, R_b, R_t, model,
generator)
for i in range(100000):
counter.update(i)
requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.celeba().loader).to(device)
img_content = encoder_HG(real_img).detach()
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(
img_content)
sparce_hm = heatmaper.forward(pred_measures.coord * 63).detach()
trainer_gan(i, real_img, img_content, sparce_hm)
if i % 3 == 0:
real_img = next(LazyLoader.celeba().loader).to(device)
content_trainer(real_img)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss_sup.json"))
print(i, coefs)
with torch.no_grad():
content_test = encoder_HG(test_img)
latent_test = style_encoder(test_img)
pred_measures = UniformMeasure2DFactory.from_heatmap(
content_test)
sparce_hm = heatmaper.forward(pred_measures.coord *
63).detach()
iwm = imgs_with_mask(test_img, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "REAL", i)
fake_img, _ = generator(sparce_hm, [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
restored = decoder(sparce_hm, latent_test)
iwm = imgs_with_mask(restored, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "RESTORED", i)
content_test_256 = nn.Upsample(
scale_factor=4)(content_test).sum(dim=1, keepdim=True)
content_test_256 = content_test_256 / content_test_256.max()
send_images_to_tensorboard(writer, content_test_256, "HM", i)
if i % 50 == 0 and i > 0:
test_loss = verka(encoder_HG)
tuner.update(test_loss)
writer.add_scalar("verka", test_loss, i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.module.state_dict(),
'd': discriminator.module.state_dict(),
'c': encoder_HG.module.state_dict(),
"s": style_encoder.state_dict()
},
f'{Paths.default.models()}/stylegan2_new_{str(i + starting_model_number).zfill(6)}.pt',
)
def forward(self, coord: Tensor):
p0, p1 = self.find_pairs(coord)
assert not torch.any(torch.isnan(p0))
assert not torch.any(torch.isnan(p1))
assert p0.max().item() <= 1 + 1e-5
assert p1.max().item() <= 1 + 1e-5
return super().forward(p0 * (self.size - 1), p1 * (self.size - 1))
if __name__ == "__main__":
from matplotlib import pyplot as plt
from dataset.probmeasure import UniformMeasure2DFactory, UniformMeasure2D01
N = 10
B = 1
size = 256
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_68").batch_repeat(10)
barycenter.coord = barycenter.coord[:, torch.randperm(68)]
# hm = CoordToGaussSkeleton(size, 5).forward(barycenter.coord)
# plt.imshow(hm[0].sum(0))
#
# plt.show()